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Progress in Applied Electrical Engineering (PAEE)

作者： Mariusz Pelc Dariusz Mikołajewski Patryk Mendoń Adrian Luckiewicz Adam Sudoł Edward Jacek Gorzelańczyk Aleksandra Kawala-Sterniuk Institute of Computer Science University of Opole Opole Poland School of Computing and Mathematical Sciences University of Greenwich London UK Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland Neuropsychological Research Unit 2nd Clinic of the Psychiatry and Psychiatric Rehabilitation Medical University in Lublin Lublin Poland Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Faculty of Natural and Technical Sciences University of Opole Opole Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

This paper deals with the problem of signals filtering using hybrid filters. Such solutions may prove to be especially beneficial in the context of biomedical signals filtering where the signals’ amplitudes are typically not very high and they can be easily affected by different kinds of disturbances. Having a number of different types of filters tuned for some specific purposes we will show how the best filter choice is affected by selecting one ML model over another.

关键词： Electrical engineering Filters Biological system modeling Biomedical communication

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Signal Averaging Noise Reduction under Dynamic Time Warping

Signal Averaging Noise Reduction under Dynamic Time Warping

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Nanotechnology in Instrumentation and Measurement (NANOFIM)

作者： Adam Łysiak Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland

ISBN: (纸本)9781665407014

Signal averaging (SA) is a procedure used to increase signal-to-noise ratio (SNR), applied in various fields, such as biomedical engineering or nanotechnology. Assuming that the noise is random and of zero mean, its average across multiple signals tends to zero. As a result, every additional measurement in the average should raise the SNR. In order for the SA to work, however, averaged signals should be in-phase. Otherwise, obtained average will not properly represent the sample signals, since their characteristic fragments will be deformed or repeated. Dynamic Time Warping (DTW) is an algorithm which can solve synchronicity problems. Using DTW, however, causes changes in the character of noise; it is no longer completely random, since its values get warped along with the desired signal. Violating the randomness of noise requirement results in weakened capability of the SA to increase the SNR. In this work, two main questions were answered: for which level of the input signals' SNR, DTW average works well, and how many sample signals should be used in order to improve SNR. Results were obtained using a modeled electrocardiogram, which has been distorted by randomly warping it and adding white noise. To average signals under the DTW, Nonlinear Alignment and Averaging Filter (NLAAF) algorithm was used.

关键词： Heuristic algorithms Nonlinear distortion Noise reduction White noise Filtering algorithms Biomedical measurement Instrumentation and measurement

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Methods and tools for automatic or semi-automatic recognition of selected emotions using machine learning algorithms

Methods and tools for automatic or semi-automatic recognitio...

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Progress in Applied Electrical Engineering (PAEE)

作者： Piotr Schneider Dariusz Mikołajewski Anna Bryniarska Magdalena Igras-Cybulska Artur Cybulski Walery Marcinowicz Maciej Janiszewski Aleksandra Kawala-Sterniuk Department of Neuroinformatics and Biomedical Engineering Institute of Computer Science Maria Curie-Sklodowska University Lublin Poland Hishoo Sp. z o.o Gdynia Poland Faculty of Computer Science Kazimierz Wielki University Bydgoszcz Poland Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

automatic or semi-automatic recognition of selected emotions using Machine Learning (ML) algorithms requires practical, fast and accurate implementation of the steps involved in acquiring the actual signals (e.g. speech, facial expression, posture and body movements), recording them in the form of an optimal feature vector/matrix, correctly interpreting the above data and reproducing and generalising this process within the ML system. Depending on the accuracy and speed required and the degree of its ‘invasiveness’ (e.g. by attaching sensors to the user’s body), single or multi-modal interpretations of biometric signals associated with different groups of emotions are possible. This group of studies, despite their relative non-invasiveness, requires the caution prescribed by the Artificial Intelligence (AI) Act and RODO Act and the possibility of being replaced (if necessary) by alternatives. This article aims to analyse the opportunities and risks associated with this group of technologies, with a particular focus on directions for further development.

关键词： Emotion recognition Machine learning algorithms Accuracy Video on demand Speech recognition Machine learning Feature extraction Recording Convolutional neural networks Web sites

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The OPS-SAT benchmark for detecting anomalies in satellite telemetry

arXiv

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arXiv 2024年

作者： Ruszczak, Bogdan Kotowski, Krzysztof Evans, David Nalepa, Jakub Faculty of Electrical Engineering Automatic Control and Informatics Department of Informatics Opole University of Technology Prószkowska Str. 76 Opole45-758 Poland Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Akademicka Str. 16 Gliwice44-100 Poland KP Labs Bojkowska Str. 37J Gliwice44-100 Poland European Space Agency ESOC Robert-Bosch-Str. 5 Darmstadt64293 Germany

Detecting anomalous events in satellite telemetry is a critical task in space operations. This task, however, is extremely time-consuming, error-prone and human dependent, thus automated data-driven anomaly detection algorithms have been emerging at a steady pace. However, there are no publicly available datasets of real satellite telemetry accompanied with the ground-truth annotations that could be used to train and verify anomaly detection supervised models. In this article, we address this research gap and introduce the AI-ready benchmark dataset (OPSSAT-AD) containing the telemetry data acquired on board OPS-SAT—a CubeSat mission which has been operated by the European Space Agency which has come to an end during the night of 22–23 May 2024 (CEST). The dataset is accompanied with the baseline results obtained using 30 supervised and unsupervised classic and deep machine learning algorithms for anomaly detection. They were trained and validated using the training-test dataset split introduced in this work, and we present a suggested set of quality metrics which should be always calculated to confront the new algorithms for anomaly detection while exploiting OPSSAT-AD. We believe that this work may become an important step toward building a fair, reproducible and objective validation procedure that can be used to quantify the capabilities of the emerging anomaly detection techniques in an unbiased and fully transparent way. © 2024, CC BY.

关键词： Anomaly detection

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Machine Learning-Based Cascade Filtering System for fNIRS Data Analysis

Machine Learning-Based Cascade Filtering System for fNIRS Da...

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Progress in Applied Electrical Engineering (PAEE)

作者： Mariusz Pelc Dariusz Mikołajewski Tuukka Ruotsalo Luis A. Leiva Adam Sudoł Edward Jacek Gorzelańczyk Adam Łysiak Aleksandra Kawala-Sterniuk Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland University of Helsinki Finland University of Copenhagen Denmark Department of Computer Science University of Luxembourg Luxembourg Luxembourg Faculty of Natural and Technical Sciences University of Opole Opole Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland

This paper presents a preliminary study on the use of machine learning-based methods to select the appropriate parameters of cascade filters in the analysis of brain signals recorded using functional infrared spectroscopy (fNIRS), which shows the level of oxygenation in the brain and, unlike EEG signals (showing electrical brain activity), are less prone to potential interference, disturbances or artifacts occurrence.

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Analysis and Prediction of Outdoor Human Mobility Using Collaborative Learning

Analysis and Prediction of Outdoor Human Mobility Using Coll...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Ioana-Valentina Marin Eduard-Claudiu Ciurezu Radu-Ioan Ciobanu Ciprian Dobre Faculty of Automatic Control and Computers National University of Science and Technology Politehnica Bucharest National Institute for Research and Development in Informatics Bucharest Romania

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

Movement is fundamental to human existence. In today’s digital age, our ability to travel shapes our social interactions, cultural experiences, and even our health. Understanding and predicting how people move outdoors is crucial for a variety of reasons. Thus, it has easily become an area of research for tech-savvy people who aim to build digital tools that use human mobility data to study the dynamics of people. Human mobility in outdoor scenarios refers to the movement of people outside their homes, cities, or countries. Moreover, as the advancement of technology significantly shapes people’s lives, they are constantly connected on different social networks and unconsciously produce substantial amounts of data by exposing their daily activities to these networks. Thus, harnessing the large volume of data extracted through mobile apps and beyond, allows for improved tourism services, urban planning modalities or brings additional help in studying the migration of masses of people in case of natural disasters. Thus, the current paper aims to describe the concept of prediction and analysis applied in the field of human mobility, from the types of data that are used, to how we can obtain existing datasets and their sources, in the context of predicting the next location of a person or urban agglomeration in a given geographical area described by the data fetched. We finally argue the utilization of federated learning in the context of outdoor human mobility.

关键词： Shape Federated learning Social networking (online) Disasters Urban planning Information age Mobile applications

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Validation of Real-world Measurements Through Ray Tracing Simulation for 5G Urban Scenarios

Validation of Real-world Measurements Through Ray Tracing Si...

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International Symposium on Electronics and Telecommunications (ISETC)

作者： Andreea V. Militaru Razvan G. Lazar Carlos Pascal Ciprian R. Comsa Ion Bogdan Constantin F. Caruntu Department of Electronics Telecommunications and Information Technology Department of Automatic Control and Applied Informatics Gheorghe Asachi Technical University of Iasi Iasi Romania

ISBN: (数字)9798350390865

ISBN: (纸本)9798350390872

This paper presents an approach to validate ray tracing simulations using real-world measurements in 5G urban scenarios. The measurements were obtained by traversing specific routes and recording parameters such as position, Signal-to-Interference-plus-Noise Ratio (SINR), Reference Signal Received Power (RSRP), and Reference Signal Received Quality (RSRQ) using the Quectel RM500Q 5G module as user equipment (UE). Subsequently, these real-world measurements are used to validate a MATLAB-based ray tracing simulation. Simulating such scenarios in a controlled environment allows for easy manipulation and study of various parameters, enabling compre-hensive verification of the simulator's accuracy. The advantage of this approach lies in its ability to ensure the reliability of the simulation tool, enabling robust testing of diverse scenarios and parameters for deployments in real-world use cases.

关键词： Power measurement 5G mobile communication Transmitters Simulation Ray tracing Telecommunications Recording Reliability Testing Signal to noise ratio

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Vehicle type recognition based on audio data 58

Vehicle type recognition based on audio data

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58th Hawaii International Conference on System Sciences, HICSS 2025

作者： Kobiela, Dariusz Hajdasz, Michal Erezman, Mateusz Nurzyńska, Karolina Zaporowski, Szymon Kurowski, Adam Weichbroth, Pawel Department of Software Engineering Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Multimedia Systems Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Algorithmics and Software Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9780998133188

Identifying different vehicle types can help manage traffic more efficiently, reduce congestion, and improve public safety. This study aims to create a classification model that can recognize vehicle types based on the sound of passing vehicles. To achieve this, a database of raw audio files containing 1763 samples from two sources was assembled. The time-domain signals were converted to a time-frequency representation using the short-time Fourier transform to generate Mel Spectrograms. Mel-frequency Cepstral Coefficients (MFCCs) were also generated using the discrete cosine transform. In our experiments we compared these approaches. Since the data was imbalanced we applied online augmentation. Based on the literature review, we chose a Convolutional Neural Network (CNN) classifier because it is particularly well suited for analyzing large datasets due to its automatic feature extraction, parameter sharing and sparsity. The results showed that Mel Spectrograms were more effective for audio data preprocessing in this particular use case, achieving the highest accuracy of 0.875 and the highest f1-score of 0.877 compared to MFCCs. © 2025 IEEE Computer Society. All rights reserved.

关键词： Image coding

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Input and state constrained inverse optimal control with application to power networks

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IFAC-PapersOnLine 2023年第2期56卷 5451-5456页

作者： Taouba Kaouther Jouini Zhiyong Sun Venkatraman Renganathan Veit Hagenmeyer Institute of Automation and Applied Informatics (IAI) at Karlsruhe Institute of Technology Germany Department of Electrical Engineering Eindhoven University of Technology Netherlands Department of Automatic Control LTH Lund University Sweden

We study input and state constrained inverse optimal control problems starting from a stabilizing controller with a control Lyapunov function, where the goal is to make the controller an explicit solution of the resulting constrained optimal control problem. For an appropriate cost design and initial states for which a sublevel set of the Lyapunov function is contained in the state constraint set and the initial input lies on an ellipsoid inside the input constraint set, we show that the stabilizing controller solves the constrained optimal control problem. Compared to the state-of-the-art, we avoid solving nonlinear optimization problems evaluated pointwise, i.e., for every state, or in a repetitive fashion, i.e., at each time step. We apply our theoretical results to study the angular droop control studied in (Jouini et al., 2022) of an inverter-based power network. For this, we accommodate the constraints on the angle and power generation and exemplify our approach through a two-inverter case study.

关键词： Optimal control Non-Linear control Systems Networked Systems Power and Energy Systems

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Energy performance of self-powered green IoT nodes

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Frontiers in Energy Research 2024年 12卷

作者： Kuaban, Godlove Suila Czachórski, Tadeusz Gelenbe, Erol Pecka, Piotr Sharma, Sapana Singh, Pradeep Nkemeni, Valery Czekalski, Piotr Gliwice Poland Haryana India Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology University of Buea Buea Cameroon Faculty of Automatic Control Electronic and Informatics Silesian University of Technology Gliwice Gliwice Poland

The widespread adoption of the Internet of Things (IoT) partly depends on the successful design and deployment of IoT nodes that can operate for several years without any service outage and the need to replace their energy storage systems (ESSs) (e.g., battery, capacitor, or supercapacitor) when all the stored energy is depleted or when the cycle life of the ESSs is reached. Replacing batteries in the case of large-scale IoT networks and nodes located in places that are hard to reach is very challenging and costly, requiring the design of IoT nodes that can operate for several years without the need for human intervention. One such example is the deployment of IoT nodes in large agricultural fields (for soil or crop monitoring) or a long-distance pipeline (for pipeline monitoring). In this paper, we investigated the practical implications of imposing energy-saving thresholds on the energy performance metrics of green IoT nodes. We propose an energy packet-based model for the evaluation of the energy performance of a green IoT node with the possibility of switching the node to energy-saving regimes on the fly when the energy content of the ESS reaches defined thresholds. Configuring single or multiple thresholds improves the energy performance of the node significantly (e.g., increases the lifetime of the node and reduces the probability of service outage and energy wastage), and the value of the threshold(s) should be carefully chosen. The energy performance of the IoT node can also be improved by dimensioning the energy harvesting system to ensure that the node operates for several years without running out of energy (e.g., maximizing the lifetime of the nodes and minimizing the probability of service outage and energy wastage). Copyright © 2024 Kuaban, Czachórski, Gelenbe, Pecka, Sharma, Singh, Nkemeni and Czekalski.

关键词： Energy efficiency

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